Customer Relationship Management (CRM) systems play an important role in personalized sales and marketing strategies with the goal of creating business opportunities. These systems can be used to search the Internet for articles that may relate to contacts of the users of the systems for the purpose of enhancing communication between the users and their contacts. Generally, the searches are directed toward keywords or phrases that may be associated with a contact. These systems initiate the searches for contact related articles at the demand of the user. Once the demand is given by the user to search for contact related articles, the systems access Internet sites and scan those sites for articles that contain/match the desired keywords. After the systems have retrieved any potentially relevant articles and matched them with the particular contacts according to the keywords, the user may then review those articles to determine whether to forward such articles onto the associated contact. However, the problem with current CRM systems that search the Internet for articles is that the searches return results that are often outdated. For example, a search for “Vince Smith” may return results including a 15 year old article. Moreover, each time a search is performed for “Vince Smith,” the same 15 year old article (e.g., that which was previously reviewed and eliminated by the user) would be returned with the results. Such searching results in numerous irrelevant articles and causes the user of such a system much time in review/filtering efforts.
Further, conventional CRM systems fail to record and store any interaction/correspondence between the user and its contacts regarding the related articles or other correspondence, or alert the user of needed correspondence. Accordingly, systems and methods for establishing and managing relationships with contacts that provide the user information for contacts and feedback with respect to contacts are needed.